Aug 2026· Formosa Journal of Computer and Information Science· Vol 5, pp. 229-242· 0 citations
TL;DR
The results showed that the Support Vector Machine algorithm had better performance than Decision Tree in classifying the sentiment of social media users X towards the Free Nutritious Meal Program.
Abstract
The research stages include a preprocessing process consisting of cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. Furthermore, the TF-IDF method is used to extract text features so that the data can be represented in numerical form. The dataset was then divided into training data and test data with an 80:20 ratio. The model evaluation was carried out using a confusion matrix by paying attention to the values of accuracy, precision, recall, and F1-Score. The results showed that the Support Vector Machine algorithm had better performance than Decision Tree in classifying the sentiment of social media users X towards the Free Nutritious Meal Program. The SVM algorithm obtained an accuracy score of 75.42%, precision of 74.65%, recall of 75.42%, and F1-Score of 74.96%. Meanwhile, the Decision Tree algorithm produced an accuracy value of 72.08%, precision of 73.33%, recall of 72.08%, and F1-Score of 72.59%.
The Makan Bergizi Gratis Program (MBG) became one of the widely discussed public issues on platform X and generated diverse responses from users. These responses included supportive, critical, and neutral opinions, making sentiment analysis relevant for understanding public opinion toward the program. This study compar...
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Sentiment analysis was conducted to examine public perceptions of the Free Nutritious Meal (Makan Bergizi Gratis, MBG) Program and identify prevailing opinion trends surrounding the policy. The analysis was initially conducted on 5,118 social media posts collected from X (formerly Twitter). After the preprocessing stag...
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